Using Neural Networks for Candidate Selection and Well Performance Prediction in Water-Shutoff Treatments Using Polymer Gels - A Field-Case Study
- Alireza Saeedi (Chevron Corp.) | Kyle V. Camarda (The University of Kansas) | Jenn-Tai Liang (U. of Kansas)
- Document ID
- Society of Petroleum Engineers
- SPE Production & Operations
- Publication Date
- November 2007
- Document Type
- Journal Paper
- 417 - 424
- 2007. Society of Petroleum Engineers
- 5.1 Reservoir Characterisation, 5.1.5 Geologic Modeling, 1.6 Drilling Operations, 5.5 Reservoir Simulation, 7.6.6 Artificial Intelligence, 4.6 Natural Gas, 5.6.1 Open hole/cased hole log analysis, 1.10.1 Drill string components and drilling tools (tubulars, jars, subs, stabilisers, reamers, etc), 4.3 Flow Assurance, 4.1.5 Processing Equipment, 6.1.5 Human Resources, Competence and Training, 3 Production and Well Operations, 2.2.2 Perforating, 4.1.2 Separation and Treating, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation
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Using actual field cases, a neural network model was developed to identify candidate wells and predict well performance for water shutoff treatments using polymer gels. A feed forward back propagation algorithm was used to develop the neural networks. The before and after treatment data for 22 wells treated with polymer gels in the Arbuckle formation in central Kansas were used to train and verify the neural networks.
Polymers and gels have been used extensively in field applications to suppress excess water production and improve oil productivity. Field experience has demonstrated that candidate-well selection is critical to the success of gel-polymer treatments. To date, most candidate-well selections are on the basis of anecdotal screening guidelines, which often results in inconsistent treatment outcomes. With only pretreatment well data as input parameters, the neural networks developed in this work can accurately predict the post-treatment cumulative oil production of the well one month after treatment with an average error of 16%, and the post-treatment cumulative oil production three months after treatment with an average error of 10%. This is a dramatic improvement over the current method of using anecdotal screening guidelines for candidate-well selections.
This method represents a major breakthrough where the candidate selection can now be on the basis of the accurate predictions of treatment outcomes without having to use complicated reservoir models to simulate the well performance after treatment.
Excess water production is a major issue in oil field operations worldwide, currently averaging three barrels of water for each barrel of oil produced (Bailey et al. 2000). The situation is even worse in the US where more than seven barrels of water are produced for each barrel of oil (EPA 1999). The annual cost of treating and disposal of this water is estimated to be USD 40 billion (Bailey et al. 2000). Water shutoff and conformance control, therefore, represents a significant financial and environmental challenge/incentive for the petroleum industry. Polymers and gels have been used extensively in field applications to suppress excess water production and improve oil productivity (Seright et al. 2003). Field experience has demonstrated that candidate-well selection is critical to the success of gel-polymer treatments (Seright et al. 2003). To date, most candidate-well selections are on the basis of anecdotal screening guidelines, which often results in inconsistent treatment outcomes (Seright et al. 2003).
Reservoir simulation can potentially be used as a screening tool to predict the post-treatment performance of a candidate well using the pre-treatment historical data (Barati et al. 2006). However, this method is usually expensive and also requires extensive knowledge of the target reservoir (including the rock and fluid properties) the historical production data, and the geological reservoir model. Unfortunately, they are not always available for older reservoirs where the well records are often incomplete or lost (Barati et al. 2006).
Another method that has been investigated is to correlate the historical pre- and post-treatment performance data of the wells treated with polymer gels in the target reservoir. In this method, multivariate analysis is used to correlate the post-treatment performance of the treated wells with the pre-treatment data such as the geographical location of the wells, the depth of the wells, and the production history of the wells, and so on. The correlation could then be used as a predictive tool for candidate selection in the target reservoir. The problem with this method is that the physical processes involved in gel polymer treatment downhole are too complex to be accurately represented by correlations generated between pre- and post-treatment data using multivariate analysis (Alhajeri et al. 2006).
Prediction of the performance of a well after treatment, using the pre-treatment data, is a pattern recognition problem. Neural networks have shown great capabilities in solving pattern recognition problems (Ali 1994; Mohaghegh 1995; Ahmed et al. 1997). The objective of this study is to develop a methodology using neural networks to identify candidate wells on the basis of the predicted outcomes for gel-polymer treatments. The before and after treatment data for 22 wells treated with polymer gels in the Arbuckle formation in central Kansas were used to develop the neural networks (Saeedi 2005).
The Arbuckle formation is the main oil producer in Kansas, responsible for approximately 36% (~2.2 billion barrels) of the total produced oil in Kansas (Franseen et al. 1999) (see Fig. 1). Arbuckle reservoirs are fracture-controlled karstic reservoirs with porosity and permeability influenced by basement structural patterns and subaerial exposures. The subaerial exposure has resulted in weathering and secondary dissolution of the upper beds of the Arbuckle. It is believed that these processes have significantly increased porosity and permeability and created petroleum reservoirs in these strata. (Franseen et al. 1999). Shallow-shelf dolomites predominantly constitute the Arbuckle formation. Porosity of the Arbuckle reservoirs is enhanced by the dolomitization process (Franseen et al. 1999). Most of the Arbuckle's oil and gas zones are perforated in the top 25 ft of the Arbuckle, while some are perforated at a depth of 25 to 50 ft within the formation (Franseen et al. 1999). High initial oil productivity, a rapid decline in oil production rate, and the production of large amounts of water at high water to oil ratios (WOR) are characteristics of Arbuckle wells (Willhite and Pancake 2004). On the basis of these characteristics, Arbuckle reservoirs have been visualized as a column of oil on top of a strong aquifer. To prevent water coning, most of the wells in the Arbuckle were drilled relatively shallow into the formation(less than 10 ft) and completed open hole (Franseen et al. 1999). Because of the absence of field cores and the lack of well log data for the full productive intervals, the reservoir characteristics of the Arbuckle formation are not well understood.
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